YoVDO

Panel Data Analysis with R

Offered By: Coursera Project Network via Coursera

Tags

Data Analysis Courses R Programming Courses Econometrics Courses Ordinary Least Squares Courses

Course Description

Overview

In this 1-hour long project-based course, you will learn how to conduct Panel Data (Regression) Analysis. You will receive step-by-step instructions to analyze the 'RENTAL' dataset from 'Introductory Econometrics: A Modern Approach' by Wooldridge using R Studio. In this project, we will discuss three models namely, Ordinary Least Square (OLS), Fixed effects (FE) and Random effects (RE) in brief and check which one fits the model best. You will also learn some additional diagnostic tests which were not required for this example but are useful for other panel datasets (especially, macro panels). Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.

Syllabus

  • Project Overview
    • Welcome to Panel Data with R! In this guided project you will learn how to conduct Panel Data Regressions. You will receive step-by-step instructions to analyze a sample dataset in R Studio. We will work with 'RENTAL' dataset from 'Introductory Econometrics: A Modern Approach' by Wooldridge. In this project, we will discuss three models namely, Ordinary Least Square (OLS), Fixed effects (FE) and Random effects (RE) in brief and check which one fits the model best. You will also learn some additional diagnostic tests which were not required for this example but are useful for other panel datasets (especially, macro panels).

Taught by

Barsha Saha

Related Courses

Introduction to Computational Finance and Financial Econometrics
University of Washington via Coursera
Эконометрика (Econometrics)
Higher School of Economics via Coursera
مبادىء الاقتصاد القياسي وتطبيقاته باستخدام برنامج التحليل SPSS الاحصائي
Rwaq (رواق)
Econometrics: Methods and Applications
Erasmus University Rotterdam via Coursera
Técnicas Cuantitativas y Cualitativas para la Investigación
Universitat Politècnica de València via edX